The present disclosure relates to systems and methods for learning tasks and, a novel system and method for complicated learning problems with both feature heterogeneity and task heterogeneity.
Many real-world problems exhibit dual-heterogeneity. To be specific, a single learning task might have features in multiple views (i.e., feature heterogeneity); different learning tasks might be related with each other through one or more shared views (features) (i.e., task heterogeneity). For example, sentiment classification for movie reviews and for political blog posts are two related tasks. They both have the word features. However, political blog posts may have additional features based on the social network of the blog users. Another example is depicted as an illustration in FIG. 1 directed to multi-lingual web image annotation 10, where images 12 collected from Chinese web sites and images 15 collected from English web sites both have content-based features (image features, e.g., as represented by a color histogram), and they also have task-specific features, i.e., surrounding texts 22 in Chinese and surrounding texts 25 in English, respectively.
Neither multi-task learning nor multi-view learning alone is optimal for such complicated learning problems.
As known, the basic idea of multi-view learning is to make use of the consistency among different views to achieve better performance. In multi-task learning, people model task relatedness in various ways.
Existing multi-task learning explores the relatedness with other tasks, but disregards the consistency among different views of a single task; whereas existing multi-view learning ignores the label information from other related tasks.
There does not exist an effective learning method to fully explore both the feature heterogeneity and the task heterogeneity simultaneously. This is partially due to the fact that existing multi-task learning and multi-view learning algorithms adopt quite different methodologies.
It would be highly desirable to provide a system and method that provides for and solves novel Multi-Task Multi-View learning problems.